#001 Import and Plot data for one stock:¶

Define and import data, save as CSV¶

Import libraries

  • Link to Pandas Documentation: https://pandas.pydata.org/docs/index.html

  • Link to datareader Documentation: https://pandas-datareader.readthedocs.io/en/latest/index.html

  • Link to Yf Documentation: https://pandas-datareader.readthedocs.io/en/latest/readers/yahoo.html

  • Link to Plotly Documentation: https://plotly.com/python/

In [1]:
#    !pip install pandas
#    !pip install pandas-datareader
#    !pip install yfinance
#    !pip install datetime
#    !pip install plotly_express
In [2]:
import pandas as pd
from pandas_datareader import data as pdr
import numpy as np
import yfinance as yf
import datetime as dt
import plotly.express as px
import plotly.graph_objects as go
In [3]:
# Define the start and end dates, last 5 years
end = dt.datetime.now()
start = end - dt.timedelta(days = 365*5)
In [4]:
# define Tickers
tk = input('Enter the ticker code: ')
tickers = [tk]
file_name = tk + ".csv"

yf.pdr_override()

Enter the ticker code: SPY
In [5]:
#obtain data and save as CSV
df = pdr.get_data_yahoo(tickers, start = start, end = end)
df.to_csv(file_name)
df.head(2)

[*********************100%%**********************]  1 of 1 completed
Out[5]:
Open High Low Close Adj Close Volume
Date
2018-11-12 277.190002 277.459991 271.98999 272.570007 250.745529 99673600
2018-11-13 273.089996 275.329987 271.25000 272.059998 250.276337 98176600

Import CSV and test values¶

In [6]:
#read CSV file
stock_df = pd.read_csv(file_name)
In [7]:
# Test for null values, and show info
stock_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1258 entries, 0 to 1257
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       1258 non-null   object 
 1   Open       1258 non-null   float64
 2   High       1258 non-null   float64
 3   Low        1258 non-null   float64
 4   Close      1258 non-null   float64
 5   Adj Close  1258 non-null   float64
 6   Volume     1258 non-null   int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 68.9+ KB

Calculate Daily Returns from one stock, and insert on DataFrame¶

In [8]:
#insert 'Daily Return' column
stock_df['Daily Return'] = stock_df['Adj Close'].pct_change(1) * 100

# replace the first row, changes null for 0
stock_df['Daily Return'].replace(np.nan, 0, inplace = True)
stock_df
Out[8]:
Date Open High Low Close Adj Close Volume Daily Return
0 2018-11-12 277.190002 277.459991 271.989990 272.570007 250.745529 99673600 0.000000
1 2018-11-13 273.089996 275.329987 271.250000 272.059998 250.276337 98176600 -0.187119
2 2018-11-14 274.160004 274.609985 268.450012 270.200012 248.565231 125335900 -0.683686
3 2018-11-15 268.779999 273.540009 267.010010 273.019989 251.159409 135101400 1.043661
4 2018-11-16 271.790009 274.750000 271.209991 273.730011 251.812607 126668000 0.260073
... ... ... ... ... ... ... ... ...
1253 2023-11-06 435.470001 436.149994 433.679993 435.690002 435.690002 67831700 0.230049
1254 2023-11-07 435.690002 437.589996 434.510010 436.929993 436.929993 64256100 0.284604
1255 2023-11-08 437.549988 438.089996 434.869995 437.250000 437.250000 61746000 0.073240
1256 2023-11-09 438.429993 438.470001 433.399994 433.839996 433.839996 83174400 -0.779875
1257 2023-11-10 435.980011 440.929993 433.829987 440.609985 440.609985 89462200 1.560481

1258 rows × 8 columns

In [9]:
stock_df.describe().round(2)
Out[9]:
Open High Low Close Adj Close Volume Daily Return
count 1258.00 1258.00 1258.00 1258.00 1258.00 1.258000e+03 1258.00
mean 369.52 371.87 367.00 369.59 356.92 8.579414e+07 0.05
std 63.99 64.10 63.84 63.99 68.18 4.241795e+07 1.34
min 228.19 229.68 218.26 222.95 211.44 2.027000e+07 -10.94
25% 306.26 308.49 302.95 307.03 289.18 5.922118e+07 -0.53
50% 384.43 387.63 380.91 384.32 375.25 7.639710e+07 0.07
75% 424.54 427.18 421.15 424.89 416.17 9.793685e+07 0.73
max 479.22 479.98 476.06 477.71 464.92 3.922207e+08 9.06

Ploting Results¶

Used Plotly Express for data visualization

In [10]:
# Define a function that performs interactive data visualization using Plotly Express
def plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    # starts with 1, to skip the date column
    
    for i in df.columns[1:]:
        # Add range slider
        fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df['Date'], y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
    fig.show()
In [11]:
# Ploting AdjClose
plotly_data(stock_df.iloc[:, [0 , 5]], (tk + ' - Ajusted Closing Price[$]'))

# Ploting Trade Volume
plotly_data(stock_df.iloc[:, [0 , 6]], (tk +' - Trading Volume'))

# Ploting Percentage Daily Return
plotly_data(stock_df.iloc[:, [0 , 7]], (tk +' - Percentage Daily Return [%]'))
In [ ]: